EEA data: Flottengrenzwerte
library(httr)
library(tidyjson)
library(readr)
library(dplyr)
library(data.table)Test getting the EEA stuff
tbl %>% content(as="parsed")
{html_document}
<html>
[1] <head>\n<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">\n<title>Overflow or underflow in the arithmetic operation ...
[2] <body bgcolor="white">\r\n\r\n <span><h1>Server Error in '/App/SqlEndpoint' Application.<hr width="100%" size="1" color="s ...First, take a look at some of these variables.
What are their proper names?
eeaGet <- function(query){
tryCatch(return({
GET("https://discodata.eea.europa.eu/sql",
query = list(query=query))%>%
content(as="parsed") %>%
(function(x){x$results}) %>%
spread_all
}), error = function(e){print(paste("problem retrieving",query))})
}
all.vars <- names(eeaGet("SELECT TOP 1 * FROM [CO2Emission].[latest].[co2cars]"))
all.vars [1] "document.id" "ID" "MS" "Mp" "VFN" "Mh" "Man"
[8] "MMS" "TAN" "T" "Va" "Ve" "Mk" "Cn"
[15] "Ct" "Cr" "m (kg)" "Mt" "Enedc (g/km)" "Ewltp (g/km)" "W (mm)"
[22] "At1 (mm)" "At2 (mm)" "Ft" "Fm" "ec (cm3)" "ep (KW)" "z (Wh/km)"
[29] "It" "Ernedc (g/km)" "Erwltp (g/km)" "De" "Vf" "r" "year"
[36] "Status" "version_file" "E (g/km)" "Er (g/km)" "..JSON" What are the distinct categories in each variable we’re looking at?
queryDistinct <- function(var){
query = paste0("SELECT DISTINCT [",
var,
"] FROM [CO2Emission].[latest].[co2cars]")
print(query)
tryCatch(return (eeaGet(query) %>% pull(var)), error = function(e){print(paste("problem retrieving",var))})
}
vars <- c("Ft", "Fm", "Mf (kg)", "Cr", "Mh", "Man", "Mk")
sapply(vars, queryDistinct)[1] "SELECT DISTINCT [Ft] FROM [CO2Emission].[latest].[co2cars]"
[1] "SELECT DISTINCT [Fm] FROM [CO2Emission].[latest].[co2cars]"
[1] "SELECT DISTINCT [Mf (kg)] FROM [CO2Emission].[latest].[co2cars]"
[1] "problem retrieving SELECT DISTINCT [Mf (kg)] FROM [CO2Emission].[latest].[co2cars]"
[1] "problem retrieving Mf (kg)"
[1] "SELECT DISTINCT [Cr] FROM [CO2Emission].[latest].[co2cars]"
[1] "SELECT DISTINCT [Mh] FROM [CO2Emission].[latest].[co2cars]"
[1] "SELECT DISTINCT [Man] FROM [CO2Emission].[latest].[co2cars]"
[1] "SELECT DISTINCT [Mk] FROM [CO2Emission].[latest].[co2cars]"
$Ft
[1] NA "" "Biodiesel" "CNG" "Diesel" "diesel/electric" "Diesel-electric" "E85" "electric"
[10] "HYBRID/PETROL/E" "Hydrogen" "lpg" "NG" "NG_biomethane" "ng-biomethane" "Other" "Petrol" "PETROL PHEV"
[19] "Petrol/Electric" "PETROL-ELECTRIC" "Petrol-Gas" "unknown"
$Fm
[1] NA "" "2" "Alimentazione Sconosciuta" "B"
[6] "E" "F" "H" "M" "n"
[11] "na" "P"
$`Mf (kg)`
[1] "problem retrieving Mf (kg)"
$Cr
[1] NA "" "M1" "M1G"
$Mh
[1] "10" "2" "5"
[4] "AA-IVA" "AA-NSS" "AC CARS"
[7] "ADIDOR" "ALFA ROMEO" "ALPINA"
[10] "ALPINE" "ANHUI" "ARTEGA"
[13] "ASTON MARTIN" "ASTON MARTIN LAGONDA LTD" "AUDI AG"
[16] "AUDI HUNGARIA" "AUDI SPORT" "AUTOMOBILES CITROEN"
[19] "AUTOMOBILES DANGEL" "AUTOMOBILES PEUGEOT" "AVTOVAZ"
[22] "BAYERISCHE MOTOREN WERKE AG" "BEE" "BEE BEE"
[25] "BENTLEY" "BLUECAR" "BLUECAR ITALY"
[28] "BMW AG" "BMW GMBH" "BMW M GMBH"
[31] "BORGWARD" "BRABUS" "BUGATTI"
[34] "BYD" "CATERHAM" "CECOMP"
[37] "CECOMP S.P.A." "CHEVROLET ITALIA" "CHONGQING LIFAN"
[40] "CHRYSLER" "CHRYSLER GROUP LLC" "CNG TECHNIK"
[43] "CNG-TECHNIK" "DACIA" "DAIHATSU"
[46] "DAIHATSU MOTOR CO" "DAIMLER AG" "DALLARA"
[49] "DFSK" "DONGFENG " "DONGFENG MOTOR CORPORATION"
[52] "DONKERVOORT" "DONKEVOORT" "DR AUTOMOBILES"
[55] "DR AUTOMOBILES SRL" "DR MOTOR" "DR MOTOR COMPANY"
[58] "Duplicate" "E-GO" "FERRARI"
[61] "FIAT GROUP" "FIAT GROUP AUTOMOBILES SPA" "FISKER"
[64] "FORD INDIA" "FORD MOTOR AUSTRALIA" "FORD MOTOR BRASIL"
[67] "FORD MOTOR COMPANY" "FORD WERKE GMBH" "FORD-WERKE GMBH"
[70] "FUJI HEAVY INDUSTRIES" "FUJI HEAVY INDUSTRIES LTD" "GEELY"
[73] "GENERAL MOTORS COMPANY" "GENERAL MOTORS HOLDINGS" "GM DAEWOO AUTO U TECH COMP"
[76] "GM ITALIA" "GM KOREA" "GM Korea Company"
[79] "GREAT WALL MOTOR" "GTF INNOVATIONS" "GUMPERT"
[82] "HONDA AUTOMOBILE CHINA CO" "HONDA AUTOMOBILE THAILAND CO" "HONDA CHINA"
[85] "HONDA MOTOR CO" "HONDA OF THE UK MANUFACTURING " "HONDA THAILAND"
[88] "HONDA TURKIYE" "HONDA TURKIYE AS" "HONDA UK"
[91] "HYUNDAI" "HYUNDAI CZECH" "HYUNDAI INDIA"
[94] "HYUNDAI ASSAN " "HYUNDAI CZECH" "HYUNDAI EUROPE"
[97] "HYUNDAI INDIA" "ISUZU" "ITALDESIGN"
[100] "IVECO" "IVECO SPA" "JAGUAR"
[103] "JAGUAR CARS LTD" "JAGUAR LAND ROVER LIMITED" "JIANGLING MOTOR"
[106] "JIANGLING MOTORS" "KIA" "KIA SLOVAKIA"
[109] "KOENIGSEGG" "KTM" "LADA"
[112] "LADA FRANCE" "LAMBORGHINI" "LAND ROVER"
[115] "LEVC" "LITEX MOTORS" "LOTUS"
[118] "LOTUS GROUP PLC" "LTI CARBODIES" "MAGYAR SUZUKI"
[121] "MAGYAR SUZUKI CORPORATION LTD" "MAHINDRA" "MAN"
[124] "MARUTI SUZUKI" "MARUTI SUZUKI INDIA LTD" "MASERATI"
[127] "MASERATI SPA" "MAZDA" "MAZDA MOTOR CORPORATION"
[130] "MCLAREN" "MERCEDES AMG" "MERCEDES-AMG GMBH"
[133] "MG" "MG MOTOR" "MIA"
[136] "MICRO VETT" "MICRO-VETT" "MITSUBISHI MOTORS CORPORATION"
[139] "MITSUBISHI MOTORS CORPORATION MMC" "MITSUBISHI MOTORS CORPORTION MMC" "MITSUBISHI MOTORS EUROPE"
[142] "MITSUBISHI MOTORS EUROPE BV MME" "MITSUBISHI MOTORS THAILAND" "MORGAN"
[145] "NEVS" "NISSAN" "NISSAN INTERNATIONAL SA"
[148] "NOBLE" "OMCI" "OPEL"
[151] "OPEL AUTOMOBILE" "OSV" "OSV OPEL SPECIAL VEHICLES"
[154] "OUT OF PRODUCTION" "OUT OF SCOPE" "PAGANI"
[157] "PERODUA" "PGO" "PIAGGIO"
[160] "PORSCHE" "POTENZA SPORTS CARS" "PROTON"
[163] "PSA " "QOROS" "QUATTRO"
[166] "RADICAL MOTORSPORT" "RADICAL MOTORSPORT LTD" "RADICAL MOTOSPORT"
[169] "RENAULT" "RENAULT TRUCKS" "ROLLS ROYCE"
[172] "ROLLS-ROYCE MOTOR CARS LTD" "ROVER" "SAAB"
[175] "SAAB AUTOMOBILE AB" "SAIC" "SAIC MOTOR"
[178] "SANTANA" "SEAT" "SECMA"
[181] "SHANGHAI MAPLE" "SHIJIAZHUANG SHUANGHUAN" "SHUANGHUAN"
[184] "SKODA" "SOVAB" "SPYKER"
[187] "SSANGYONG" "SUBARU" "SUZUKI MOTOR CORPORATION"
[190] "SUZUKI THAILAND" "TAIQI" "TATA"
[193] "TAZZARI" "TECNO MECCANICA" "TESLA"
[196] "THINK GLOBAL" "TOYOTA" "TOYOTA MOTOR EUROPE "
[199] "UNKNOWN" "UNKNWON" "VAUXHALL"
[202] "VEHICULES ELECTRIQUES PININFARINA-BOLLORE S.A.S." "VEPB" "VOLKSWAGEN"
[205] "VOLVO" "WESTFIELD" "WIESMANN"
[208] "ZOTYE HOLDING NG GROUP"
$Man
[1] NA ""
[3] "101" "127"
[5] "134" "139"
[7] "Å KODA AUTO A.S." "AA_IVA"
[9] "AA_NSS" "AA-IVA"
[11] "AA-NNS" "AA-NSS"
[13] "AC CARS" "ADAM OPEL AG"
[15] "ADAM OPEL AG" "ADAM OPEL GMBH"
[17] "ADIDOR VOITURES SAS" "ADRIA MOBIL D.O.O."
[19] "ALFA ROMEO SPA" "ALPINA"
[21] "ALPINA BURKARD BOVENSIEPEN GMBH E CO KG" "AM GENERAL"
[23] "ANHUI JIANGHUAI AUTOMOBILE" "ARTEGA"
[25] "ARTEGA AUTOMOBIL GMBH E CO KG" "ASTON MARTIN"
[27] "ASTON MARTIN LAGONDA LTD" "AUDI"
[29] "AUDI AG" "AUDI AG D-85045 Ingolstadt"
[31] "AUDI AG DE 85045 Ingolsta" "AUDI AG DE-85045 Ingolstadt"
[33] "AUDI HUNGARIA" "AUDI HUNGARIA KFT."
[35] "AUDI HUNGARIA MOTOR KFT" "AUDI HUNGARIA MOTOR Kft."
[37] "AUDI HUNGARIA MOTOR KFT. HU-9027" "AUDI SPORT GMBH"
[39] "AUSTIN" "AUTOCARAVANS RIMOR"
[41] "AUTOMOBILE DACIA SA" "AUTOMOBILES CITROEN"
[43] "AUTOMOBILES DANGEL" "AUTOMOBILES DANGEL SA"
[45] "AUTOMOBILES PEUGEOT" "AUTOMOBILI LAMBORGHINI SP"
[47] "AUTOMOBILI LAMBORGHINI SPA" "AUTOMOBILIES PEUGEOT"
[49] "AUTOVAZ" "Avto VAZ"
[51] "AVTOVAZ" "AVTOVAZ JSC"
[53] "BAYERISCHE ACHSENFABRIK" "Bayerische Motoren Werke"
[55] "BAYERISCHE MOTOREN WERKE AG" "BAYERISCHE MOTOREN WERKE GMBH"
[57] "BAYERISCHE MOTOREN WERKEN AG" "BEE BEE AUTOMOTIVE"
[59] "BEIJING BORGWARD AUTOMOTIVE CO LTD" "BENTLEY"
[61] "BENTLEY MOTORS LDT" "BENTLEY MOTORS LTD"
[63] "BENTLEY MOTORS LTD." "BERTONE"
[65] "BLUECAR ITALY SRL" "BLUECAR SAS"
[67] "BMW" "BMW M, GMBH"
[69] "BMW AG" "BMW GMBH"
[71] "BMW M GMBH" "BMW UK MANUF. LTD"
[73] "BRABUS" "BUGATTI"
[75] "BUGATTI AUTOMOBILES SAS" "BYD"
[77] "BYD AUTO INDUSTRY COMPANY LIMITED" "CADILLAC"
[79] "CATERHAM" "CATERHAM CARS LIMITED"
[81] "CECOMP S.P.A." "CHEVROLET"
[83] "CHEVROLET ITALIA" "CHEVROLET ITALIA SPA"
[85] "China First Automotive Group Company" "CHONGQING LIFAN PASSENGER VEHICLE CO LTD"
[87] "CHRYSLER" "CHRYSLER CORPORATION"
[89] "CHRYSLER GROUP LLC" "CHRYSLER GROUP LLC 1000 CHRYSLER DRIVE AUBURN HILLS MICHIGAN USA 48326 2766"
[91] "CITROEN" "CNG-TECHNIK"
[93] "CNG-TECHNIK GMBH" "COMARTH"
[95] "DACIA" "DAF TRUCKS N.V."
[97] "DAIHATSU" "DAIHATSU MOTOR CO"
[99] "DAIHATSU MOTOR CO LTD" "DAIMLER"
[101] "DAIMLER AG" "DAIMLER AG STUTTGART"
[103] "DAIMLER CHRYSLER AG" "DAIMLER CHRYSLER CORP."
[105] "DAIMLER CHRYSLER CORPORATION" "DFSK MOTOR CO LTD"
[107] "DONG FENG" "DONGFENG MOTOR CORPORATION"
[109] "DONKERVOORT AUTOMOBIELEN BV" "DONKEVOORT AUTOMOBIELEN BV"
[111] "DOTTO" "DR AUTOMOBILES SRL"
[113] "DR ING HCF PORSCHE AG" "DR MOTOR COMPANY"
[115] "DR MOTOR COMPANY SRL" "DR. ING.H.C.F. PORSCHE AG"
[117] "duplicate" "E-GO MOBILE AG"
[119] "FABBRICA DALLARA SRL" "FAW HAIMA MOTOR"
[121] "FCA ITALY SPA" "FCA US LLC"
[123] "FERRARI" "FERRARI SPA"
[125] "FFORD-WERKE GMBH" "FIAT"
[127] "FIAT AUTO" "FIAT GROUP"
[129] "FIAT GROUP AUTOMOBILES" "FIAT GROUP AUTOMOBILES SP"
[131] "FIAT GROUP AUTOMOBILES SPA" "FIAT GROUP AUTOMOBILES SPA (I)"
[133] "FISKER" "FISKER AUTOMOTIVE"
[135] "FISKER AUTOMOTIVE INC" "FISKER AUTOMOTIVE INC."
[137] "FORD" "FORD - WERKE GMBH"
[139] "FORD INDIA PRIVATE LIMITED" "FORD MOTOR CO GMBH"
[141] "FORD MOTOR COMPANY" "FORD MOTOR COMPANY OF AUSTRALIA LIMITED"
[143] "FORD MOTOR COMPANY OF BRAZIL LTDA" "FORD WERKE GMBH"
[145] "FORD-WERKE AG" "FORD-WERKE GMBH"
[147] "FSO" "FUJI HEAVY IND. LTD."
[149] "FUJI HEAVY INDUSTRIE" "FUJI HEAVY INDUSTRIES"
[151] "FUJI HEAVY INDUSTRIES LTD" "FUJI HEAVY INDUSTRIES LTD."
[153] "GEELY" "GENARAL MOTORS COMPANY"
[155] "GENERAL MOTOR COMPANY" "GENERAL MOTORS"
[157] "GENERAL MOTORS COMPA" "GENERAL MOTORS COMPANY"
[159] "GENERAL MOTORS CORPORATIO" "GENERAL MOTORS HOLDINGS LLC"
[161] "GENERAL MOTORS ITALIA S.R.L." "GENERAL MOTORS ITALIA SRL"
[163] "GM DAEWOO" "GM DAEWOO AUTO"
[165] "GM DAEWOO AUTO & TECH.COMP." "GM DAEWOO AUTO AND TECHNOLOGY COMPANY"
[167] "GM DAEWOO AUTO U TECH COM" "GM DAEWOO AUTO U TECH COMP"
[169] "GM DAEWWO AUTO U TECH COMP" "GM ITALIA"
[171] "GM ITALIA SRL" "GM KOREA"
[173] "GM KOREA COMPANY" "GREAT WALL MOTOR"
[175] "GREAT WALL MOTOR COMPANY LIMITED" "GROUP LOTUS PLC"
[177] "GTF INNOVATIONS SAS" "GUMPERT"
[179] "GUMPERT SPORTWAGENMANUFAKTUR GMBH" "GWM"
[181] "HONDA" "HONDA AUTOMOBILE (CHINA) CO"
[183] "HONDA AUTOMOBILE (THAILAND)" "HONDA AUTOMOBILE CHINA CO"
[185] "HONDA AUTOMOBILE CHINA CO LTD" "HONDA AUTOMOBILE THAILAND CO"
[187] "HONDA AUTOMOBILE THAILAND CO LTD" "HONDA CHINA"
[189] "HONDA MOTOR CO" "HONDA MOTOR CO LTD"
[191] "HONDA MOTOR CO.,LTD." "HONDA MOTOR EUROPE LTD"
[193] "HONDA OF THE UK MANUFACTU" "HONDA OF THE UK MANUFACTURIN"
[195] "HONDA OF THE UK MANUFACTURING" "HONDA OF THE UK MANUFACTURING LTD"
[197] "HONDA TURKIYE" "HONDA TURKIYE AS"
[199] "HONDA UK" "HYUNDAI"
[201] "HYUNDAI ASSAN O.S." "HYUNDAI ASSAN OTOMOTIV"
[203] "HYUNDAI ASSAN OTOMOTIV SANAYI VE" "HYUNDAI ASSAN OTOMOTIV SANAYI VE TICARET AS"
[205] "HYUNDAI MOTOR" "HYUNDAI MOTOR COMPANY"
[207] "HYUNDAI MOTOR CZECH" "HYUNDAI MOTOR EUROPE GMBH"
[209] "HYUNDAI MOTOR INDIA" "HYUNDAI MOTOR INDIA LTD"
[211] "HYUNDAI MOTOR MAN.CZECH" "HYUNDAI MOTOR MANUFACTURING CZECH SRO"
[213] "ISUZU MOTORS LIMITED" "ISUZU MOTORS LTD"
[215] "ITALDESIGN GIUGIARO SPA" "IVECO S.P.A."
[217] "IVECO SPA" "JAGUAR"
[219] "JAGUAR CARS LIMITED" "JAGUAR CARS LTD"
[221] "JAGUAR LAND ROVER LIMITED" "JAGUARCARS LTD"
[223] "JEEP" "JIANGLING MOTOR HOLDING CO LTD"
[225] "JIANGLING MOTOR HOLDING CO., LTD." "JIANGLING MOTORS"
[227] "KIA" "KIA MOTOR COMPANY"
[229] "KIA MOTORS CORP." "KIA MOTORS CORPORATION"
[231] "KIA MOTORS SLOVAKIA" "KIA MOTORS SLOVAKIA SRO"
[233] "KIA SLOVAKIA SRO" "KNAUS TABBERT GMBH"
[235] "KOENIGSEGG AUTOMOTIVE AB" "KTM"
[237] "KTM-SPORTMOTORCYCLE AG" "LADA"
[239] "LADA AUTOMOBILE GMBH" "LADA FRANCE"
[241] "LADA FRANCE SAS" "LAMBORGHINI"
[243] "LAND ROVER" "Land Rover Banbuty Road Gaydon Warwick CV35 0RR UK"
[245] "LAND ROVER GROUP LTD" "LANDWIND"
[247] "LITEX MOTORS AD" "LONDON EV COMPANY"
[249] "LOTUS" "LOTUS CARS LIMITED"
[251] "LOTUS CARS LTD" "LTI CARBODIES"
[253] "MAGYAR SUZUKI" "MAGYAR SUZUKI CORP"
[255] "MAGYAR SUZUKI CORPORATION" "MAGYAR SUZUKI CORPORATION LTD"
[257] "MAHINDRA" "MAHINDRA & MAHINDRA LTD"
[259] "MAHINDRA E MAHINDRA LTD" "MAN TRUCK & BUS AG"
[261] "MARUTI SUZUKI" "MARUTI SUZUKI CORPORATION LTD"
[263] "MARUTI SUZUKI INDIA LTD" "MARUTI SUZUKI INDIA LTD."
[265] "MARUTI SUZUKI INDIA, LTD." "MASERATI"
[267] "MASERATI SPA" "MAZDA"
[269] "MAZDA MOTOR CORPORATION" "Mclaren"
[271] "MCLAREN AUTOMOTIVE LIMITE" "MCLAREN AUTOMOTIVE LIMITED"
[273] "MERCEDES" "MERCEDES AMG"
[275] "MERCEDES-AMG" "MERCEDES-AMG GMBH"
[277] "MERCEDES-AMG GMBH AFFALTE" "MERCEDES-AMG GMBH AFFALTERBACH"
[279] "MG" "MG MOTOR"
[281] "MG MOTOR UK LIMITED" "MIA"
[283] "MIA ELECTRIC" "MIA ELECTRIC SAS"
[285] "MICHALAK" "MICRO-VETT"
[287] "MICRO-VETT SPA" "MICRO-VETT SRL"
[289] "MITSHUBISHI MOTORS" "MITSUBISHI"
[291] "MITSUBISHI MOTORS" "MITSUBISHI MOTORS CO"
[293] "MITSUBISHI MOTORS CORP." "MITSUBISHI MOTORS CORPORATION"
[295] "MITSUBISHI MOTORS CORPORATION MMC" "MITSUBISHI MOTORS CORPORATION MMC"
[297] "MITSUBISHI MOTORS CORPORT" "MITSUBISHI MOTORS CORPORTION"
[299] "MITSUBISHI MOTORS CORPORTION MMC" "MITSUBISHI MOTORS EUR. B.V."
[301] "MITSUBISHI MOTORS EUROPE" "MITSUBISHI MOTORS EUROPE B.V MME"
[303] "MITSUBISHI MOTORS EUROPE BV MME" "MITSUBISHI MOTORS EUROPE BV NME"
[305] "MITSUBISHI MOTORS EUROPE MME" "MITSUBISHI MOTORS THAILAND CO LTD MMTH"
[307] "MORGAN" "MORGAN MOTOR CO LTD"
[309] "MORGAN MOTOR COMPANY" "MORGAN TECHNOLOGIES LTD"
[311] "NATIONAL ELECTRIC VEHICLE SWEDEN" "NISSAN"
[313] "NISSAN EUROPE N.V." "NISSAN INTERNATIONAL"
[315] "NISSAN INTERNATIONAL SA" "NISSAN MOTOR CO LTD"
[317] "NOBLE AUTOMOTIVE LTD" "OAO AVTOVAZ"
[319] "OMCI" "OMCI S.R.L."
[321] "OMCI SRL" "OPEL"
[323] "OPEL ADAM AG" "OPEL AUTOMOBILE GMBH"
[325] "OSV" "OSV OPEL SPECIAL VEHICLES GMBH"
[327] "OUT OF SCOPE" "PAGANI AUTOMOBILI SPA"
[329] "PENTA" "PERODUA"
[331] "PERODUA MANUFACTURING SDN BHD" "PERODUA UK LIMITED"
[333] "PERUSAHAAN OTOMOBIL NASIONAL SDN BHD" "PEUGEOT"
[335] "PGO" "PGO AUTOMOBILES"
[337] "PIAGGIO" "PIAGGIO & C SPA"
[339] "PORCHE" "PORSCHE"
[341] "PORSCHE AG" "POTENZA SPORTS CARS"
[343] "PROTON" "PSA AUTOMOBILES SA"
[345] "QOROS AUTOMOTIVE CO LTD" "QUATTRO"
[347] "QUATTRO GMBH" "quattro GmbH DE 74148 Nec"
[349] "RADICAL MOTORSPORT LTD" "RADICAL MOTOSPORT LTD"
[351] "RANGE ROVER" "RAW ENGINEERING"
[353] "RENAULT" "RENAULT S.A.S"
[355] "RENAULT S.A.S." "RENAULT SAS"
[357] "Renault SAS 13-15 Quai Alphonse Le Gallo 92110 Boulogne-Billancourt France" "RENAULT TRUCKS"
[359] "RENAULT TRUCKS SAS" "ROLLS ROYCE"
[361] "ROLLS-ROYCE MOTOR CARS LT" "ROLLS-ROYCE MOTOR CARS LTD"
[363] "ROLLS-ROYCE MOTORS CARS L" "ROLLS-ROYCE MOTORS CARS LTD"
[365] "ROVER" "ROVER GROUP LTD"
[367] "SAAB" "SAAB AUTOMOBILE AB"
[369] "SAIC MOTOR CORPORATION LIMITED" "SAIC MOTOR CORPORATION LTD"
[371] "SANTANA" "SANTANA MOTOR SA"
[373] "SANYANG" "SEA S.P.A."
[375] "SEAT" "SEAT S.A."
[377] "SEAT SA" "SECMA"
[379] "SECMA SAS" "SHANGHAI MAPLE AUTOMOBILE CO LTD"
[381] "SHIJIAZHUANG SHUANGHUAN AUTOMOBILE COMPANY" "SHUANGHUAN"
[383] "SKODA" "SKODA AUTO AS"
[385] "SMART" "SOCIETE DES AUTOMOBILES ALPINE"
[387] "SOVAB" "SPYKER AUTOMOBIELEN BV"
[389] "SSANG YONG MOTOR COMPANY " "SSANGYONG"
[391] "SSANGYONG MOTOR CO." "SSANGYONG MOTOR COMPANY"
[393] "SUBARU CORPORATION" "SUZUKI"
[395] "SUZUKI MOTOR CO." "SUZUKI MOTOR CORPORATION"
[397] "SUZUKI MOTOR THAILAND CO LTD" "TAIQI ELECTRIC VEHICLE CO. LIMITED"
[399] "TATA" "TATA MOTORS LIMITED"
[401] "TAZZARI GL SPA" "TECNO MECCANICA IMOLA SPA"
[403] "TESLA" "TESLA INC"
[405] "TESLA MOTORS LIMITED" "TESLA MOTORS LTD"
[407] "THINK" "THINK GLOBAL"
[409] "TOYOTA" "TOYOTA MOOR EUROPE NV SA"
[411] "TOYOTA MOTOR CORP." "TOYOTA MOTOR CORPORATION"
[413] "TOYOTA MOTOR EUROPE" "TOYOTA MOTOR EUROPE NV SA"
[415] "TOYOTA MOTOR EUROPE NV SA 0" "TOYOTA MOTOR EUROPE NV SA"
[417] "TOYOTA SOUTH AFRICA MOTORS LTD" "TVR"
[419] "UAZ " "UNKNOWN"
[421] "VAUXHALL" "VAZ TOGLIATTI"
[423] "VEHICULES ELECTRIQUES PININFARINA-BOLLORE S.A.S." "VEHICULES ELECTRIQUES PININFARINA-BOLLORE SAS"
[425] "VOLGA" "VOLKSWAGEN"
[427] "VOLKSWAGEN AG" "VOLKSWAGEN AG BERLINER RING 2 D-38440 WOLFSBURG"
[429] "VOLVO" "VOLVO CAR CORP."
[431] "VOLVO CAR CORPORATION" "WESTFIELD"
[433] "WESTFIELD SPORTS CARS" "WIESMANN"
[435] "WIESMANN GMBH" "without manufacturer"
[437] "ZEJIANG ZOTYE AUTOMOBILE MANUFACTURING CO., LTD" "ZHEJIANG GEELY AUTOMOBILE CO LTD"
[439] "ZOTYE HOLDING NG GROUP"
$Mk
[1] NA "" "-"
[4] " OLKSWAGEN VW" " VAUXHALL" "?KODA"
[7] "0000, VAUXHALL" "0000000000,VW" "00000EN"
[10] "050L, VAUXHALL" "05TROEN" "05YOTA"
[13] "0ERCEDES-BENZ" "0PEL, VAUXHALL" "1"
[16] "1 GMNEW" "1(GMNEW)" "1.0I"
[19] "1525" "15251523" "18002EN"
[22] "1EUGEOT" "1GMNEW" "1KODA"
[25] "1OLKSWAGEN" "1OYOTA" "2667"
[28] "270" "2ISSAN" "2OLKSWAGEN,VW"
[31] "2PEL, VAUXHALL" "3 BMW" "34 PEUGEOT"
[34] "3ISSAN" "3OLVO" "4091407"
[37] "413T" "4411434GEN" "472L, VAUXHALL"
[40] "4921481" "5011501I" "5011505"
[43] "5601560" "5721531" "706/2007"
[46] "79" "8" "A.U.D.I."
[49] "Ã…Â KODA" "AA-NSS" "AB"
[52] "ABARTH" "AC" "ACHLEITNER"
[55] "ACURA" "ADI" "ADRIA"
[58] "ADRIA MOBIL" "ALFA" "ALFA ROMEO"
[61] "ALFA ROMEO" "ALFAROMEO" "ALFA-ROMEO"
[64] "ALFAROMEOO" "ALLIED VEHICLES LTD" "ALPINA"
[67] "ALPINE" "ALU TRANS SYSTEM" "AMF-BRUNS"
[70] "API CZ" "ARIEL" "ARTEGA"
[73] "ASTON MARTIN" "ASTONMARTIN" "ASTON-MARTIN"
[76] "AUDI" "AUDI AG" "AUDI AG (D)"
[79] "AUDI HUNGARIA" "AUDI SPORT" "AURUS"
[82] "AUSTIN" "AUTOMOBILES CITROEN" "AUTOMOBILES PEUGEOT"
[85] "AUTOMOBILI LAMBORGHINI" "AUTOMOBILI LAMBORGHINI SPA" "AVTOVAZ"
[88] "B R M" "B.M.W." "BAIC"
[91] "BAVARIA" "BEE BEE" "BENTLEY"
[94] "BENTLEY CO" "BENTLEY MOTORS Ltd" "BENYE"
[97] "BERTONE" "BIERMANN" "BINZ"
[100] "BLUECAR" "BMW" "BMW AG"
[103] "BMW ALPINA" "BMW GMBH" "BMW I"
[106] "BMW MINI" "BMW,MINI" "BMW1MINI"
[109] "BMWI" "BMWMINI" "BORGWARD"
[112] "BRABUS AUTOMOTIVE" "BRONTO" "B-STYLE & FLEX-I-TRANS"
[115] "B-STYLE&FLEX-I-TRANS" "BUGATTI" "BUICK"
[118] "BURSTNER" "BUSCONCEPT" "BUS-PRESTIGE"
[121] "BYD" "CACIA" "CADILLAC"
[124] "CAMARO" "CAPRON" "CARBODIES"
[127] "CATERHAM" "CATERHAM CARS" "CATERHAM CARS LTD"
[130] "CCK-WESTFIELD" "CECOMP" "CHAMONIX"
[133] "CHANGHE" "CHERVOLET" "CHEVROLEΤ"
[136] "CHEVROLET" "Chevrolet Daewoo" "CHEVROLET GMC"
[139] "CHEVROLET GMC BUICK PONTIAC HOLDEN DAEWOO" "CHEVROLET GMC BUICK PONTIAC HOLDEN DAEWOO SUZUKI" "CHEVROLET GMC BUICK PONTIAC SUZUKI DAEWOO"
[142] "CHEVROLET GMC BUICK PONTIAC SUZUKI DAEWOO HOLDEN" "CHEVROLET GMC BUICK PONTIAC SUZUKI HOLDEN DAEWOO" "CHEVROLET,BUICK PONTIAC SUZUKI DAEWOO HOLTEN GMC"
[145] "CHEVROLET,BUICK,PONTIAC" "CHEVROLET,DAEWOO" "CHEVROLET,DAEWOO,HOLD"
[148] "CHEVROLET,GMC,BUICK,PONTIAC" "CHEVROLET,GMC,BUICK,PONTIAC,HOLDEN" "CHEVROLET,GMC,BUICK,PONTIAC,SUZUKI,DAEWOO,HOLDEN"
[151] "CHEVROLET,GMC,BUICK,PONTIAC,SUZUKI,HOLDEN,DAEWOO" "CHEVROLET,GMC,HOLDEN" "CHEVROLET,GMC,HOLDEN,DAEWOO"
[154] "CHEVROLET?GMC?HOLDEN?DAEWOO" "CHEVROLETGMCBUICKPONTIACHOLDENDAEWOO" "CHEVROLETGMCBUICKPONTIACSUZUKIDAEWOO"
[157] "CHRVROLET" "CHRYSLER" "CHRYSLER JEEP"
[160] "CI" "CITOREN" "CITROËN"
[163] "CITROEN" "CITROËN" "CITROEN DANGEL"
[166] "CITROEN DS" "CITROEN, DS" "CITROEN,DS"
[169] "CITROEN-DS" "COMARTH" "CORVETTE"
[172] "COUPE" "Crd" "CRD CAR RESEARCH & DEVELOPMENT GMBH & CO KG"
[175] "CRD CAR RESEARCH GMBH" "CUBY" "DACIA"
[178] "DACIA AUTOMOBILE SA" "DACIA-OKURA" "DAEWOO"
[181] "DAEWOO BUICK CHEVROLET PONTIAC SUZUKI HOLDEN GMC" "DAEWOO BUICK CHEVROLETPONTIACSUZUKIHOLDENGMC" "DAEWOO GM DAEWOO CHEVROLET"
[184] "DAEWOO GM DAEWOO CHEVROLET CHEVROLET DAEWOO" "DAEWOO GM DAEWOO CHEVROLETCHEVROLET DAEWOO" "DAEWOO,GMC,PONTIAC,CHEVROLET,HOLDEN,BUICK,SUZUKI"
[187] "DAF" "DAICA" "DAIHATSU"
[190] "DAIHATSU MOTOR COMPANY" "DAIMLER" "DAIMLER AG"
[193] "DAIMLER SMART" "DALLARA" "DANGEL"
[196] "DAVID BROWN" "DETHLEFFS" "DFM"
[199] "DFSK" "DFSK SOKON" "DFSK, SOKON"
[202] "DODGE" "DODGE (USA)" "DODGE (USA)"
[205] "DODGE FIAT" "DONG FENG" "DONKERVOORT"
[208] "DONKERVOORT(JD)" "DR" "DR AUTOMOBILES SRL"
[211] "DR MOTOR COMPANY" "DRINGHCFPORSCHE" "DS"
[214] "Duffy" "DURANGO" "EAGLE"
[217] "EGO" "ELNAGH" "EURA MOBIL"
[220] "FACTORY FIVE" "FAW XIALI" "FCA ITALY S.P.A S.P.A"
[223] "FERRARI" "FIAT" "FIAT - INNOCENTI"
[226] "FIAT GROUP" "FIAT/ABARTH" "FIAT/PARTNER"
[229] "FIAT/PZL-MIELEC" "FIAT-BURSTNER" "FIAT-CARTHAGO"
[232] "FIAT-DETHLEFFS" "FIAT-EURA-MOBIL" "FIAT-KABE"
[235] "FIESTA" "FISKER" "FISKER AUTOMOTIVE INC."
[238] "FOCUS" "Ford" "FORD - CNG - TECHNIK"
[241] "FORD - CNG-TECHNIK" "FORD (D)" "FORD (USA)"
[244] "FORD CNG TECHNIK" "FORD D" "FORD USA"
[247] "FORD W GMBH" "FORD WERKE" "FORD WERKE GMBH"
[250] "FORD/FORD POLSKA" "FORD/GERMAZ" "FORD/IGLOOCAR"
[253] "FORD-CNG TECHNIK" "Ford-CNG- Technik" "FORD-CNG-TECHNIK"
[256] "FORD-LMC" "FSO" "FUJI HEAVY INDUSTRIES"
[259] "FUSO" "FX" "GEELY"
[262] "GENERAL MOTORS" "GENERAL MOTORS COMPANY" "GENERAL MOTORS CORPORATIO"
[265] "GENERAL MOTORS -GMC" "GENERAL MOTORS ITALIA" "GENESIS"
[268] "GERMAN E CARS" "GIANTCO" "GINETTA"
[271] "GM CHEVROLET" "GM COACHWORK" "GM KOREA"
[274] "GMC" "GMNEW" "GOLDSCHMITT"
[277] "GREAT WALL" "GREAT WALL MOTOR" "GREAT WALL MOTOR COMPANY"
[280] "GREYT WALL" "GUMPERT" "GUMPERT AIWAYS AUTOMOBILE"
[283] "GWM" "GY" "HAIDLMAIR"
[286] "HAIMA 3" "HARRIER" "HAVAL"
[289] "HIUNDAI" "HOECKMAYR" "HOLDEN"
[292] "Honda" "HONDA CHINA" "HONDA EUR"
[295] "HONDA MOTOR CO" "HONDA TURKIYE" "HONDA UK"
[298] "HS9" "HUMBER" "HUMMER"
[301] "HUYNDAI" "Hyundai" "HYUNDAI (TR)"
[304] "HYUNDAI ASSAN " "HYUNDAI GENESIS" "HYUNDAI MOTOR"
[307] "HYUNDAI MOTOR (CZ)" "HYUNDAI MOTOR (IND)" "HYUNDAI MOTOR (KOR)"
[310] "HYUNDAI MOTOR (ROK)" "HYUNDAI MOTOR (SK)" "HYUNDAI MOTOR COMPANY"
[313] "Hyundai, Genesis" "Î’MW,MINI" "IAV"
[316] "IMPREZA" "INDIGO" "INFINITI"
[319] "INFINITI/PGD" "INFINITY" "ΜΑRUTI SUZUKI"
[322] "ΚIA" "ISUZU" "ITALDESIGN AUTOMOBILI SPECIALI"
[325] "IVECO" "IVECO - FIAT" "IVECO/IPL"
[328] "IVECO-FORD" "JAC" "Jaguar"
[331] "JAGUAR (JAGUAR CARS L" "JAGUAR CARS" "JAGUAR CARS LIMITED"
[334] "JAGUAR CARS LTD" "JAGUAR LAND ROVER" "JAGUAR LAND ROVER LIM"
[337] "JAGUAR LAND ROVER LIMIT" "JAGUAR LAND ROVER LIMITED" "JAGUAR LAND ROVER LTD"
[340] "JAGUAR LAND ROVERLIMI" "Jaguar Land RoverLimited" "JAGUAR(Jaguar Cars Limited)"
[343] "JAGUARCARSLIMITEDed" "JANGUAR LAND ROVER LI" "JEEP"
[346] "JENSEN" "JEPP" "JETTA"
[349] "JHH" "JIMINI" "K.T.M."
[352] "KGB10L" "KIA" "KIA MOTOR"
[355] "KIA MOTOR (ROK)" "KIA MOTORS" "KIA MOTORS (ROK)"
[358] "KIA Motors (SK)" "KIA SLOVAKIA" "KIVI"
[361] "KNAUS" "KOENIGSEGG" "KTM"
[364] "LADA" "LADA - VAZ" "LADA AUTOMOBILE GMBH"
[367] "LADA-VAZ" "LAIKA CARAVANS" "LAIKA CARAVANS SPA"
[370] "LAMBORGHIMI" "LAMBORGHIN" "LAMBORGHINI"
[373] "LANCIA" "LANCIA - AUTOBIANCHI" "LANCIA - CHRYSLER"
[376] "LANCIA CHRYSLER" "LANCIA,CHRYSLER" "LAND"
[379] "LAND ROVER" "LAND ROVER LD" "LAND ROVER RANGE"
[382] "LANDROVER" "LANDWIND" "LATERAL DESIGN LTD"
[385] "LAVERDA" "LEVC" "LEXUS"
[388] "LINCOLN" "LITEX MOTORS" "LMC"
[391] "LONDON TAXIS INT." "LOTUS" "LTC"
[394] "LTI VEHICLES" "LUMENEO" "LUSITANO"
[397] "M" "MAFEEC" "MAGYAR"
[400] "MAGYAR SUZUKI" "MAGYAR SUZUKI CORPORA" "MAHINDRA"
[403] "MAHINDRA (INDIEN)" "MAHINDRA LTD." "Make Unknown"
[406] "MAN" "MANITOWE" "MARCOS"
[409] "MARTIN MOTORS" "MARUTI" "MARUTI SUZUKI INDIAL"
[412] "MASERATI" "MAYBACH" "MAZDA"
[415] "MAZDA 6" "MC LAREN" "MCLAREN"
[418] "MCLOUIS" "MECEDES-BENZ" "MEGANE"
[421] "MERCDEDES-BENZ" "MERCDES BENZ" "MERCDES-AMG"
[424] "MERCEDES" "MERCEDES BENZ" "MERCEDES - BENZ"
[427] "MERCEDES AMG" "MERCEDES BENZ" "MERCEDES BENZ (D)"
[430] "Mercedes.Benz" "MERCEDESAMG" "MERCEDES-AMG"
[433] "MERCEDES-AMG GMBH" "MERCEDES-AMPetrol" "MERCEDESBENZ"
[436] "MERCEDES-BENZ" "MERCEDES-BENZ AG" "MERCEDES-BENZ BUSCONCEPT"
[439] "MERCEDES-BENZ(E)" "MERCEDES-BENZ/MBPL" "Mercedez-Benz"
[442] "MERCURY" "MERCUS" "MG"
[445] "MIA" "MICRO COMPACT CAR SMART" "MICRO VETT"
[448] "MICRO-VETT" "MINI" "MINI ONE D COUNTRYMAN"
[451] "MINIMAX" "MITSUBISH" "MITSUBISHI"
[454] "MITSUBISHI (J)" "MITSUBISHI (NL)" "MITSUBISHI (THA"
[457] "MITSUBISHI (THA)" "MITSUBISHI J" "MITSUBISHI MOTORS CORPORATION"
[460] "MITSUBISHI MOTORS THAILAND" "MITSUBISHI THA" "MITSUBISHI YEN"
[463] "MOBILVETTA" "MORGAN" "MORGAN MOTOR"
[466] "MPM MOTORS" "NILSSON" "NIO"
[469] "NISSAN" "NISSAN EUROPE S.A.S." "NOBLE"
[472] "NOBLE AUTOMOTIVE" "OCKELBO" "OLDSMOBILE"
[475] "OLKSWAGEN" "OLKSWAGEN VW" "OMAVALMISTATUD"
[478] "OMCI" "OPEL" "OPEL AUTOMOBILE GMBH"
[481] "OPEL VAUXHALL" "OPEL," "OPEL, VAUXHALL"
[484] "OPEL, VAUXHALL" "OPEL/CARPOL" "OPEN"
[487] "OTHER BRITISH" "OTHER ITALIAN" "ÖVRIGA"
[490] "PAGANI" "PÖSSL" "PENTA"
[493] "PERODUA" "PEUGEOT" "PEUGEOT (F)"
[496] "PEUGEOT DANGEL" "PEUGOT" "PGO"
[499] "PGO AUTOMOBILES" "PIAGGIO" "PIAGGIO (I)"
[502] "PILOTE" "PIRSO" "PLYMOUTH"
[505] "POCLAIN VEHICULES" "POESSL" "PONTIAC"
[508] "PORCHE" "PORSCHE" "Porsche 2020000"
[511] "PORSCHE 2600000" "Porshce" "POSSL"
[514] "PÖSSL" "PROTON" "PSA AUTOMOBILES"
[517] "PUGEOT" "QOROS" "QUATRO"
[520] "QUATTRO" "QUOROS AUTOMOTIVE" "R1EAMG"
[523] "R1ECLS" "RADICAL" "RADICAL MOTORSPORT"
[526] "RADICAL SPORTSCARS" "RANGE ROVER" "RANGE-ROVER"
[529] "RAW" "RELIANT" "RENAULT"
[532] "RENAULT OU DACIA" "RENAULT TECH" "RENAULT TRUCKS"
[535] "RENAULT/CARPOL" "RENAULT/GRUAU" "REULT"
[538] "RFMOTO" "RIMOR" "ROLLER TEAM"
[541] "ROLLS ROYCE" "ROLLS-ROYCE" "ROLLY-ROYCE"
[544] "ROVER" "ROVER CARS" "SAAB"
[547] "SAM" "SANTANA" "SAVIEM"
[550] "SCANIA" "SEAT" "SECMA"
[553] "SECMAÂ " "SEIKEL" "SHENZHEN BYD AUTO"
[556] "SHUANGHUAN" "SHUANGHUAN AUTO" "SICAR"
[559] "SKODA" "ŠKODA" "SKODA AUTO S.A."
[562] "SMART" "SOCIETE DES AUTOMOBILES ALPINE SAA" "SOMAC"
[565] "SONO MOTORS GMBH" "SORTIMO SPEEDWAVE" "SPORTS TOURE"
[568] "SSANG YONG" "SSANGJONG" "SSANGY0NG"
[571] "SSANGYONG" "STX" "SUBARU"
[574] "SUBARU/IMPREZA" "SUBARU/LEGACY" "SUBURU"
[577] "SUKIDA" "SUSUKI" "SUZUKI"
[580] "SUZUKI (J)" "SUZUKI EUR" "SUZUKI MOTOR"
[583] "SUZUKI MOTOR CORPORATION" "SYLVA" "SYMC"
[586] "TAIQI" "TATA" "TAZZARI"
[589] "TESLA" "TESLA MOTORS" "THE LONDON TAXI COMPANY"
[592] "THINK" "THUNDERBOLT" "TOTH ES FIAI"
[595] "TOYOTA" "TRIGANO" "TRIPOD"
[598] "TRISTAR" "TRIUMPH" "TVR"
[601] "UAZ" "ULTIMA" "VAUXHALL"
[604] "VAZ" "VEICOLI" "VEPB"
[607] "VOGKSWAGEN" "VOKSWAGEN VW" "VOLGA"
[610] "VOLKS WAGEN" "VOLKS WAGEN , VW" "VOLKS WAGEN ,VW"
[613] "VOLKSAWGEN, VW" "VOLKSVAGEN" "VOLKSWAGEN"
[616] "VOLKSWAGEN COMM" "VOLKSWAGEN VW" "VOLKSWAGEN - VW"
[619] "VOLKSWAGEN ,VW" "VOLKSWAGEN AG" "VOLKSWAGEN MOTORSPORT"
[622] "VOLKSWAGEN V W" "VOLKSWAGEN VW" "VOLKSWAGEN, VW"
[625] "VOLKSWAGEN, VW" "VOLKSWAGEN,VW" "VOLKSWAGEN,VW AG"
[628] "VOLKSWAGEN/AMZ-KUTNO" "VOLKSWAGEN/ZIMNY" "VOLKSWAGENVW"
[631] "VOLKSWAGEN-VW" "VOLKSWAGENVW VW" "VOLKSWAGENVWVW"
[634] "VOLKSWAGENVWW" "VOLVO" "VOLVO CAR CORPORATION"
[637] "VOLVO/CARRUS" "VOSCO" "VOVLO"
[640] "VW" "VW - VOLKSWAGEN" "WAZ"
[643] "WEINSBERG" "WESTFALIA" "WESTFIELD"
[646] "WIESMANN" "WILLYS" "WILLYS/WILLYS-OVERLAND"
[649] "WOLKSWAGEN" "WOLKSWAGEN,VW" "X-BOW"
[652] "X-RAID" "ZENVO" "ZERO"
[655] "ZOTYE" "ZVW30L" Okay! Get average mass [m (kg)] and emissions [Ewltp (g/km), Enedc (g/km)] and sum of vehicles sold [r] for each fuel type [Ft] for each year [year]
mass.emissions.by.ft <- GET("https://discodata.eea.europa.eu/sql",
query = list(query="SELECT year, Ft,
AVG(CAST([m (kg)] AS FLOAT)) mass_avg,
SUM(CAST([m (kg)] AS FLOAT)) mass_tot,
AVG(CAST([Ewltp (g/km)] AS FLOAT)) wltp_avg,
SUM(CAST([Ewltp (g/km)] AS FLOAT)) wltp_tot,
AVG(CAST([Enedc (g/km)] AS FLOAT)) enedc_avg,
SUM(CAST([Enedc (g/km)] AS FLOAT)) enedc_tot,
SUM(r) n_total
FROM [CO2Emission].[latest].[co2cars]
GROUP BY year, Ft"))%>%
content(as="parsed") %>%
(function(x){x$results}) %>%
spread_all %>%
mutate(Ft=tolower(Ft))
Try a little viz–over time by category
mass.emissions.by.ft %>%
mutate(Ft=tolower(Ft)) %>%
group_by(Ft,year) %>%
summarize(mass_tot=sum(mass_tot,na.rm = TRUE),
wltp_tot=sum(wltp_tot,na.rm = TRUE),
enedc_tot=sum(enedc_tot,na.rm = TRUE),
n_tot=sum(n_total,na.rm = TRUE)) ->
summarized`summarise()` regrouping output by 'Ft' (override with `.groups` argument)summarized %>% distinct(Ft,year)try better palette…
#install.packages("pals")
library(pals)library(ggplot2)
# total mass
ggplot(mass.emissions.by.ft, aes(x=year, y=mass_tot)) +
geom_point(aes(color=Ft)) +
scale_fill_gradientn(colours = stepped())
average mass…
ggplot(mass.emissions.by.ft, aes(x=year, mass_avg)) +
geom_point(aes(color=Ft))
average emissions…
# WLTP
ggplot(mass.emissions.by.ft, aes(x=year, y=wltp_avg)) +
geom_point(aes(color=Ft))
# NEDC
ggplot(mass.emissions.by.ft, aes(x=year, y=enedc_avg)) +
geom_point(aes(color=Ft)) +
scale_color_discrete()
It seems like one of the biggest issues is the different names for same category. Let’s fix that.
mass.emissions.by.ft %>% distinct(Ft) %>% pull(Ft) [1] "petrol" "electric" "diesel" "biodiesel"
[5] "ng-biomethane" "" "e85" "lpg"
[9] "ng_biomethane" "unknown" "other" NA
[13] "cng" "petrol-electric" "diesel-electric" "hydrogen"
[17] "petrol-gas" "petrol/electric" "petrol phev" "hybrid/petrol/e"
[21] "electric " "diesel/electric" "ng"
mass.emissions.by.ft %>%
mutate(Ft = recode(na_if(Ft,""),
"ng-biomethane" = "rng",
"ng_biomethane" = "rng",
"diesel-electric" = "hybrid_diesel",
"diesel/electric" = "hybrid_diesel",
"hybrid/petrol/e" = "hybrid_petrol",
.missing = "unknown",
"petrol-electric" = "hybrid_petrol",
"petrol-gas" = "petrol_gas",
"petrol phev" = "p_hybrid_petrol",
"petrol/electric" = "hybrid_petrol",
"NA" = "unknown"
)) %>%
ggplot(aes(x=year, y=mass_avg)) +
geom_point(aes(color=Ft)) +
scale_color_manual(values=glasbey())
Now, try bubble plot of mass and emissions over time by manufacturer.
First, check which of these manufacturer categories is most reasonable
lapply(mans, function(df){
dt <- as.data.table(df)
print(names(dt)[2])
print(paste("Missing:",dt[is.na(dt[[2]])|dt[[2]]==""|dt[[2]]=="-"|dt[[2]]=="UNKNOWN", sum(n)]))
print(paste("Categories:", nrow(dt)))
return (NULL)
})
[1] "man"
[1] "Missing: 341615"
[1] "Categories: 439"
[1] "mh"
[1] "Missing: 63045"
[1] "Categories: 208"
[1] "Mk"
[1] "Missing: 224829"
[1] "Categories: 656"
[1] "MMS"
[1] "Missing: 2183155"
[1] "Categories: 11024"
[1] "MP"
[1] "Missing: 7619095"
[1] "Categories: 36"
[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL“Mh” seems to have the fewest missing values, and also the fewest distinct categories, so lets use that one. get the appropriate data–average mass and emissions by year and manufacturer:
mass.tbl <- eeaGet("SELECT year, Mh,
AVG(CAST([m (kg)] AS FLOAT)) mass_avg,
AVG(CAST([Ewltp (g/km)] AS FLOAT)) wltp_avg,
AVG(CAST([Enedc (g/km)] AS FLOAT)) enedc_avg,
SUM(r) n_sales
FROM [CO2Emission].[latest].[co2cars]
GROUP BY year, Mh")
# get rid of the JSON column--I don't know a better way :(
# mass.tbl <- as.tibble(as.data.table(mass.df))How are our manufacturers looking, though?
We need to clean the manufacturers
I’m going to give OpenRefine a try here. We have to the the same variables, and regroup based on sales numbers
fwrite(mass.dt, "/Users/maita.schade/Documents/Work/Flottengrenzwerte/out/eea_mass_emis.csv")
Error in fwrite(mass.dt, "/Users/maita.schade/Documents/Work/Flottengrenzwerte/out/eea_mass_emis.csv") :
Permission denied: '/Users/maita.schade/Documents/Work/Flottengrenzwerte/out/eea_mass_emis.csv'. Failed to open existing file for writing. Do you have write permission to it? Is this Windows and does another process such as Excel have it open?Seems like openrefine isn’t very amenable to matching from beginning. I’ll try to cluster these manufacturers.
mass.tbl.clean %>%
group_by(man.name.clean) %>%
summarise(total = sum(n_sales))
`summarise()` ungrouping output (override with `.groups` argument)Graphics for Europe
# just for 2017... what is going on with the fits?!!
# All is well now, lm and geom_smooth match up perfectly. I was so confused :'-D
# mass.tbl.2017 <- filter(mass.tbl.clean, year==2017)
# lm.2017 <- lm("wltp_avg ~ mass_avg", data = mass.tbl.2017) #weights = n_sales,
# mass.tbl.2017 %>%
# mutate(lm.fit = predict.lm(lm.2017, .)) ->
# mass.tbl.2017.fit
#
# p <- ggplot(mass.tbl.2017.fit) +
# geom_point(alpha=0.5, aes(x=mass_avg, y=wltp_avg, size=n_sales)) +
# geom_line(aes(x=mass_avg, y=lm.fit), color="red") +
# stat_smooth(method = "lm", mapping = aes(x=mass_avg, y=wltp_avg, weight = NULL), fullrange=T)
# pMake a bubble graph!
# then, make the actual plots:
(ggplot(filter(mass.tbl.clean, year>2016), aes_string(label="man.name.clean", x=x, y=y, size="n_sales")) +
facet_grid(cols = vars(year),drop = T) +
geom_point(alpha=0.5) +
ylab("Specific emissions fleet average (g/km)") +
xlab("Average vehicle mass (kg)") +
labs(size = "Sales by manufacturer") +
ggtitle(title) +
geom_smooth(method = "lm", mapping = aes(weight = n_sales)) +
#stat_smooth(aes(outfit=fit<<-..y..))+
geom_text(data=emissions.ratio, aes(x=x_max, y=y_max, label=round(slope, digits = 3)), size=5, color=1, nudge_x = -100)) %>%
ggplotly
`geom_smooth()` using formula 'y ~ x'
Removed 96 rows containing non-finite values (stat_smooth).Error in nchar(plot$labels$title %||% "") :
cannot coerce type 'closure' to vector of type 'character'Graphs for Europe
eeaBubbleGraph(mass.tbl.clean, "mass_avg", "wltp_avg", "WLTP emissions v. mass (EU manufacturer fleet averages)")
All elements of `...` must be named.
Did you want `data = c(man.name.clean, mass_avg, wltp_avg, enedc_avg, n_sales)`?`geom_smooth()` using formula 'y ~ x'
Removed 96 rows containing non-finite values (stat_smooth).eeaBubbleGraph(mass.tbl.clean, "mass_avg", "enedc_avg")
All elements of `...` must be named.
Did you want `data = c(man.name.clean, mass_avg, wltp_avg, enedc_avg, n_sales)`?`geom_smooth()` using formula 'y ~ x'
Removed 1 rows containing non-finite values (stat_smooth).Germany only
Das gleiche, aber nur für Deutschland. Wie sind die Länder ausgewiesen?
Get data just for Germany, nicely grouped
mass.de.tbl %>%
eeaCleanNames %>%
eeaRegroupByCleanNames ->
mass.de.tbl.clean
`summarise()` regrouping output by 'man.name.clean' (override with `.groups` argument)Make bubble graphs for Germany! WLTP first…
eeaBubbleGraph(mass.de.tbl.clean, "mass_avg", "wltp_avg", "WLTP emissions v. mass (DE manufacturer fleet averages)")
All elements of `...` must be named.
Did you want `data = c(man.name.clean, mass_avg, wltp_avg, enedc_avg, n_sales)`?`geom_smooth()` using formula 'y ~ x'
Removed 96 rows containing non-finite values (stat_smooth).eeaBubbleGraph(mass.de.tbl.clean, "mass_avg", "enedc_avg", "NEDC emissions v. mass (DE manufacturer fleet averages)")
All elements of `...` must be named.
Did you want `data = c(man.name.clean, mass_avg, wltp_avg, enedc_avg, n_sales)`?`geom_smooth()` using formula 'y ~ x'
Removed 1 rows containing non-finite values (stat_smooth)."Z (Wh/km)" %>% (function(var){
print(var)
df <- eeaGet(paste0("SELECT [", var, "], COUNT(ID) n
FROM [CO2Emission].[latest].[co2cars]
GROUP BY [", var, "]"))
print(names(df))
return(df)
}) %>%
(function(df){
dt <- as.data.table(df)
print(names(dt)[2])
print(paste("Missing:",dt[is.na(dt[[2]])|dt[[2]]==""|dt[[2]]=="-"|dt[[2]]=="UNKNOWN", sum(n)]))
print(paste("Categories:", nrow(dt)))
return (NULL)
})
[1] "Z (Wh/km)"
[1] "document.id" "Z (Wh/km)" "n" "..JSON"
[1] "Z (Wh/km)"
[1] "Missing: 60556106"
[1] "Categories: 252"
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y helpful!
# install.packages("stringdist")
# library(stringdist)
# uniqueman <- unique(as.character(mass.dt$Mh))
# distanceman <- stringdistmatrix(uniqueman,uniqueman,method = "jw")
# rownames(distanceman) <- uniqueman
# hc <- hclust(as.dist(distanceman))
# plot(hc)
# rect.hclust(hc,k=20)

eeaCleanNames <- function(df){ # just a function to rewrite some of these names
  df %>%
    mutate(name = toupper(Mh)) %>%
    mutate(name = str_replace(name, "GENERAL MOTORS?", "GM")) %>%
    mutate(name = str_replace(name, "LAND ROVER","LAND-ROVER")) %>%
    mutate(name = str_replace(name, "-WERKE|-AMG","")) %>%
    mutate(name = str_replace(name, "ROLLS ROYCE","ROLLS-ROYCE")) %>%
    mutate(name = str_replace(name, "VEHICULES ELECTRIQUES PININFARINA-BOLLORE","PININFARINA-BOLLORE")) %>% 
    mutate(name = str_replace(name, "UNKNWON","UNKNOWN")) %>%
    mutate(name = str_replace(name, "GREAT WALL","GREAT-WALL")) %>%
    mutate(name = str_replace(name, "SHANGHAI MAPLE","MAPLE")) %>%
    mutate(name = str_replace(name, "^ROVER","TATA")) %>%
    mutate(name = str_replace(name, "AUTOMOBILES?\\s?","")) %>%
    mutate(name = str_replace(name, "MOTOR CORPORATION\\s?","")) %>%
    mutate(name = str_replace(name, "(GENERAL )MOTOR COMPANY\\s?","")) %>%
    mutate(name = str_replace(name, "MOTORS?\\s?","")) %>%
    mutate(name = if_else(str_detect(Mh, "SHUANGHUAN"), "SHUANGHUAN", name)) %>%
    mutate(name = if_else(str_detect(Mh, "BAYERISCHE MOTOREN WERKE AG"), "BMW", name)) %>%
    mutate(first = str_extract(name, "(^[^\\s]*)(\\s|$)")) %>%
    mutate(first = str_squish(str_trim(first))) %>%
    mutate(man.name.clean = first, first = NULL, name = NULL) 
}

eeaRegroupByCleanNames <- function(df){
  df %>% # renamed cleanly
    group_by(man.name.clean, year) %>%
    summarise(mass_avg = weighted.mean(mass_avg, n_sales), # and then re-group and re-summarize according to the new names
              wltp_avg = weighted.mean(wltp_avg, n_sales),
              enedc_avg = weighted.mean(enedc_avg, n_sales),
              n_sales = sum(n_sales)) %>%
    ungroup
}
# looking at specific odd names
# man.names %>%
#   filter(str_detect(Mh, "LIFAN"))

mass.tbl %>% 
  eeaCleanNames %>%
  eeaRegroupByCleanNames->
mass.tbl.clean

# looking at specific odd cases
# filter(mass.tbl, str_starts(Mh, "ALPINA"))

mass.tbl.clean %>%
  group_by(man.name.clean) %>%
  summarise(total = sum(n_sales))

```
### Graphics for Europe

```{r}
# just for 2017... what is going on with the fits?!!
# All is well now, lm and geom_smooth match up perfectly. I was so confused :'-D
# mass.tbl.2017 <- filter(mass.tbl.clean, year==2017)
# lm.2017 <- lm("wltp_avg ~ mass_avg", data = mass.tbl.2017) #weights = n_sales, 
# mass.tbl.2017 %>%
#   mutate(lm.fit = predict.lm(lm.2017, .)) ->
#   mass.tbl.2017.fit
# 
# p <- ggplot(mass.tbl.2017.fit) +
#   geom_point(alpha=0.5, aes(x=mass_avg, y=wltp_avg, size=n_sales)) +
#   geom_line(aes(x=mass_avg, y=lm.fit), color="red") +
#   stat_smooth(method = "lm", mapping = aes(x=mass_avg, y=wltp_avg, weight = NULL), fullrange=T)
# p
```


Make a bubble graph!
```{r}
eeaBubbleGraph <- function(df, x, y, title){ # we'd like to make bubble size a var as well, but it's complicated!

  # first, the fit to be able to label them in the graphs: 
df %>%
  nest(-year) %>% # fold everything by year
  filter(year>2016) %>% # use only years after 2016, because no emissions data before then
  mutate(fit = map(data, ~lm(paste(y,"~",x), data = ., weights = n_sales)),
         results = map(fit, tidy)) %>% # fit and clean the fit for each year
  unnest(results) %>% # unfold the fit results
  pivot_wider(id_cols=c(year, data), values_from = estimate, names_from = term) %>% # make a column for each fit result of interest
  rename(slope=mass_avg, intercept=`(Intercept)`) %>% # 
  mutate(x_max = map(data, ~(max(.$mass_avg)))) %>% 
  unnest(x_max) %>%
  mutate(y_max = x_max*slope + intercept, x_max = x_max-100, man.name.clean = "name", mass_avg = 1, wltp_avg = 1, n_sales = 1) ->
  emissions.ratio

# then, make the actual plots:
(ggplot(filter(mass.tbl.clean, year>2016), aes_string(label="man.name.clean", x=x, y=y, size="n_sales")) +
  facet_grid(cols = vars(year),drop = T) +
  geom_point(alpha=0.5) +
  ylab("Specific emissions fleet average (g/km)") +
  xlab("Average vehicle mass (kg)") + 
  labs(size = "Sales by manufacturer") +
  ggtitle(title) +
  geom_smooth(method = "lm", mapping = aes(weight = n_sales)) +
  #stat_smooth(aes(outfit=fit<<-..y..))+
  geom_text(data=emissions.ratio, aes(x=x_max, y=y_max, label=round(slope, digits = 3)), size=5, nudge_x = -100)) %>%
  ggplotly
}
```

Graphs for Europe
```{r}
eeaBubbleGraph(mass.tbl.clean, "mass_avg", "wltp_avg", "WLTP emissions v. mass (EU manufacturer fleet averages)")
```
```{r}
eeaBubbleGraph(mass.tbl.clean, "mass_avg", "enedc_avg", "NEDC emissions v. mass (EU manufacturer fleet averages)")
```

### Germany only

Das gleiche, aber nur für Deutschland.
Wie sind die Länder ausgewiesen?

```{r}
eeaGet("SELECT DISTINCT(MS) FROM [CO2Emission].[latest].[co2cars]")
```

Get data just for Germany, nicely grouped
```{r}
mass.de.tbl <- eeaGet("SELECT year, Mh,
AVG(CAST([m (kg)] AS FLOAT)) mass_avg, 
AVG(CAST([Ewltp (g/km)] AS FLOAT)) wltp_avg,
AVG(CAST([Enedc (g/km)] AS FLOAT)) enedc_avg,
SUM(r) n_sales
FROM [CO2Emission].[latest].[co2cars]
WHERE MS='DE'
GROUP BY year, Mh")

mass.de.tbl %>%
  eeaCleanNames %>%
  eeaRegroupByCleanNames ->
  mass.de.tbl.clean
```

Make bubble graphs for Germany!
WLTP first...
```{r}
eeaBubbleGraph(mass.de.tbl.clean, "mass_avg", "wltp_avg", "WLTP emissions v. mass (DE manufacturer fleet averages)")
```
```{r}
eeaBubbleGraph(mass.de.tbl.clean, "mass_avg", "enedc_avg", "NEDC emissions v. mass (DE manufacturer fleet averages)")
```
```{r}
"Z (Wh/km)" %>% (function(var){
                 print(var)
                 df <- eeaGet(paste0("SELECT [", var, "], COUNT(ID) n 
       FROM [CO2Emission].[latest].[co2cars]
       GROUP BY [", var, "]"))
                 print(names(df))
                 return(df)
               }) %>%
(function(df){
  dt <- as.data.table(df)
  print(names(dt)[2])
  print(paste("Missing:",dt[is.na(dt[[2]])|dt[[2]]==""|dt[[2]]=="-"|dt[[2]]=="UNKNOWN", sum(n)]))
  print(paste("Categories:", nrow(dt)))
  return (NULL)
})

```

